Identification and validation of stemness-related lncRNA prognostic signature for breast cancer

Li, Xiaoying; Li, Yang; Yu, Xinmiao; Jin, Feng

doi:10.1186/s12967-020-02497-4

Cited by 76 publications

(66 citation statements)

References 39 publications

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“…However, multiomic angles have to be focused which primarily included genomics, epigenetics, and transcriptomic profiles. On the basis of the dataset, Shen and colleagues established an lncRNA panel associated with immunological signatures to stably predict the prognosis of breast cancer [16], which is consistent with several studies [17,18], and supported that the phenotypes could be the results of molecular heterogeneity. From the immunologic perspective, we indicated that inherent heterogeneity could lead to divergent prognosis and curated three robust TME subtypes for breast cancer, of which the potential mechanisms leading to this kind of differentiation remained to be explored.…”

Section: Discussionsupporting

confidence: 66%

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Wang

2021

BioMed Research International

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Objective. To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice. Methods. For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model. Results. Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles. Conclusions. We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.

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Section: Discussionsupporting

confidence: 66%

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Wang

2021

BioMed Research International

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“…The AUCs of the signature and the nomogram in our study at 1-, 3-, and 5-years were 0.719, 0.762, 0.742 and 0.836, 0.767, 0.792 respectively. Table 4 showed that the AUCs of four prognostic signature including 12 stemness-related lncRNA signature (0.813 at 5 years) ( 47 ), 11 immune-related lncRNA signature (0.836 at 5 years) ( 52 ), 27 immune-related gene signature (0.844 at 5 years) ( 54 ) and four methylated gene signature (0.791 at 5 years) ( 61 ) were distinctly higher than that of other biomarkers. Moreover, our signature also performed better in the prediction of BC patients’ OS than the signature based on the hallmarks related to autophagy ( 48 ), tumor microenvironment (immune, stromal, and proliferation) ( 49 ), tumor mutation burden ( 50 ), hypoxia ( 51 ), DNA repair ( 55 ), lncRNA ( 56 ) and miRNA ( 57 , 58 ).…”

Section: Resultsmentioning

confidence: 99%

“…The studies ( 47 , 48 , 52 , 54 , 61 ) we included were that the model was built based on the entire TCGA cohort and involved all types of breast cancer, not a certain subtype. The final results showed that our signature and another four prognostic signature including 12 stemness-related lncRNA signature ( 47 ), 11 immune-related lncRNA signature ( 52 ), 27 immune-related gene signature ( 54 ) and four methylated gene signature ( 61 ) performed better in the prediction of BC patients’ OS than the signature based on the hallmarks related to autophagy ( 48 ), tumor microenvironment (immune, stromal, and proliferation) ( 49 ), tumor mutation burden ( 50 ), hypoxia ( 51 ), DNA repair ( 55 ), lncRNA ( 56 ) and miRNA ( 57 , 58 ). Considering that the clinical application cost of our model may be lower than that of the two gene models [12 stemness-related lncRNA signature ( 47 ) and 27 immune-related gene signature ( 54 )] and glycolysis is closely related to the prognosis of BC, our signature may be necessary to enrich the clinical prediction methods.…”

Section: Discussionmentioning

confidence: 99%

“…To further explore the predictive ability of our nomogram, a comparison was performed among several significant molecular signatures which were employed for predicting OS of BC patients. The studies (47,48,52,54,61) we included were that the model was built based on the entire TCGA cohort and involved all types of breast cancer, not a certain subtype. The final results showed that our signature and another four prognostic signature including 12 stemness-related lncRNA signature (47), 11 immune-related lncRNA signature (52), 27 immune-related gene signature (54) and four methylated gene signature (61) performed better in the prediction of BC patients' OS than the signature based on the hallmarks related to autophagy (48), tumor microenvironment (immune, stromal, and proliferation) (49), tumor mutation burden (50), hypoxia (51), DNA repair (55), lncRNA (56) and miRNA (57,58).…”

Section: Discussionmentioning

confidence: 99%

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Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

et al. 2021

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To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

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“…Among them, 986 patients with complete follow-up information and survival time ≥ 30 days and 539 patients with complete clinicopathological data were selected into subsequent analyses. The clinical features are detailed in Table 1 [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

A novel autophagy-related genes prognostic risk model and validation of autophagy-related oncogene VPS35 in breast cancer

Cao

et al. 2021

Cancer Cell Int

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Background Accumulating evidence implies that autophagy plays a critical role in breast cancer development and progression. It is crucial to screen out autophagy-related encoding genes (ARGs) with prognostic value in breast cancer and reveal their biological properties in the aggressiveness of breast cancer. Methods Univariate and multivariate Cox proportional hazards analyses were used to identify a prognostic risk model of ARGs from The Cancer Genome Atlas (TCGA). Kaplan–Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Western blot and immunohistochemistry (IHC) were conducted to assess the expression of VPS35 (one of ARGs in risk model). CCK8, Colony formation assay, Transwell migration/invasion assays and autophagy flux assay were used to confirm biological function of VPS35 in breast cancer. Results In this study, the prognostic risk model consisting of six ARGs (VPS35, TRIM21, PRKAB2, RUFY4, MAP1LC3A and LARP1) in breast cancer were identified. The risk model was further verified as a novel independent prognostic factor for breast cancer patients. We also clarified that vacuolar protein sorting-associated protein 35 (VPS35), one of ARGs in the risk model, was upregulated in breast cancer samples and cell lines. VPS35 overexpression was correlated with more aggressive phenotype of breast cancer and indicated worse prognosis in both progression-free survival and overall survival analyses. Meanwhile, VPS35 knockdown inhibited breast cancer cell proliferation, migration and invasion, suggesting that VPS35 promoted the progression of breast cancer. VPS35 silence also influenced autophagy process, indicating that VPS35 was essential for autophagy completion. Conclusion Taken together, the six ARGs risk model has a remarkably prognostic value for breast cancer. Among them, VPS35 might exert as a significant oncogenic and prognostic factor for breast cancer and could be a promising autophagy-related therapeutic target in clinical practice.

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Identification and validation of stemness-related lncRNA prognostic signature for breast cancer

Cited by 76 publications

References 39 publications

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

A novel autophagy-related genes prognostic risk model and validation of autophagy-related oncogene VPS35 in breast cancer

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